Zobrazeno 1 - 10
of 52 426
pro vyhledávání: '"A. A. Baran"'
Autor:
Nitsch, Maximilian, Maffi, Lorenzo, Baran, Virgil V., Souto, Rubén Seoane, Paaske, Jens, Leijnse, Martin, Burrello, Michele
The Majorana tetron is a prototypical topological qubit stemming from the ground state degeneracy of a superconducting island hosting four Majorana modes. This degeneracy manifests as an effective non-local spin degree of freedom, whose most paradigm
Externí odkaz:
http://arxiv.org/abs/2411.11981
Majorana modes can be engineered in arrays where quantum dots (QDs) are coupled via grounded superconductors, effectively realizing an artificial Kitaev chain. Minimal Kitaev chains, composed by two QDs, can host fully-localized Majorana modes at dis
Externí odkaz:
http://arxiv.org/abs/2411.07068
Autor:
Grönquist, Peter, Bhattacharjee, Deblina, Aydemir, Bahar, Ozaydin, Baran, Zhang, Tong, Salzmann, Mathieu, Süsstrunk, Sabine
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital humanities, there is a growing demand to leverage technology for dive
Externí odkaz:
http://arxiv.org/abs/2410.20459
Autor:
Atalar, Baran, Joe-Wong, Carlee
We consider the contextual combinatorial bandit setting where in each round, the learning agent, e.g., a recommender system, selects a subset of "arms," e.g., products, and observes rewards for both the individual base arms, which are a function of k
Externí odkaz:
http://arxiv.org/abs/2410.14586
Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting, and decrease computation time
Externí odkaz:
http://arxiv.org/abs/2410.06815
Autor:
Coussat, A., Krzemien, W., Baran, J., Parzych, S., Beyene, E., Chug, N., Curceanu, C., Czerwiński, E., Das, M., Dulski, K., Eliyan, K. V., Jasińska, B., Kacprzak, K., Kapłon, Ł., Klimaszewski, K., Korcyl, G., Kozik, T., Kubat, K., Kumar, D., Vendan, A. Kunimal, Lisowski, E., Lisowski, F., Mędrala-Sowa, J., Moyo, S., Mryka, W., Niedźwiecki, S., Pandey, P., del Rio, E. Perez, Raczyński, L., Rädler, M., Sharma, S., Skurzok, M., Stępień, E. Ł., Tayefi, K., Tanty, P., Wiślicki, W., Moskal, P.
In positron emission tomography acquisition (PET), sensitivity along a line of response can vary due to crystal geometrical arrangements in the scanner and/or detector inefficiencies, leading to severe artefacts in the reconstructed image. To mitigat
Externí odkaz:
http://arxiv.org/abs/2410.00669
Molecular arrangement in the chiral smectic phases of the glassforming (S)-4'-(1-methylheptylcarbonyl)biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]benzoate is investigated by X-ray diffraction. An increased correlation length of
Externí odkaz:
http://arxiv.org/abs/2409.08654
Convolutional Neural Networks (CNNs) have made remarkable strides; however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as 'attacks',
Externí odkaz:
http://arxiv.org/abs/2409.03458
Publikováno v:
Proc. SPIE, 2024, 13097, Adaptive Optics Systems IX, 1309776
We present the Python pipeline that was developed to simulate the high contrast mode images of the MICADO instrument. This mode will comprise three classical Lyot coronagraphs with different occulting spot sizes, one vector apodized phase plate, and
Externí odkaz:
http://arxiv.org/abs/2408.17123
How can Transformers model and learn enumerative geometry? What is a robust procedure for using Transformers in abductive knowledge discovery within a mathematician-machine collaboration? In this work, we introduce a new paradigm in computational enu
Externí odkaz:
http://arxiv.org/abs/2408.14915